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Apache HugeGraph-Computer is a comprehensive graph computing solution providing two complementary systems for different deployment scenarios:

• Vermeer (Go): High-performance in-memory computing engine for single-machine deployments
• Computer (Java): Distributed BSP/Pregel framework for large-scale cluster computing

graph TB
 subgraph HugeGraph-Computer
 subgraph Vermeer["Vermeer (Go) - In-Memory Engine"]
 VM[Master :6688] --> VW1[Worker 1 :6789]
 VM --> VW2[Worker 2 :6789]
 VM --> VW3[Worker N :6789]
 end
 subgraph Computer["Computer (Java) - Distributed BSP"]
 CM[Master Service] --> CW1[Worker Pod 1]
 CM --> CW2[Worker Pod 2]
 CM --> CW3[Worker Pod N]
 end
 end
 HG[(HugeGraph Server)] <--> Vermeer
 HG <--> Computer
 style Vermeer fill:#e1f5fe
 style Computer fill:#fff3e0

Vermeer is designed with a Master-Worker architecture optimized for high-performance in-memory graph computing:

graph TB
 subgraph Client["Client Layer"]
 API[REST API Client]
 UI[Web UI Dashboard]
 end
 subgraph Master["Master Node"]
 HTTP[HTTP Server :6688]
 GRPC_M[gRPC Server :6689]
 GM[Graph Manager]
 TM[Task Manager]
 WM[Worker Manager]
 SCH[Scheduler]
 end
 subgraph Workers["Worker Nodes"]
 W1[Worker 1 :6789]
 W2[Worker 2 :6789]
 W3[Worker N :6789]
 end
 subgraph DataSources["Data Sources"]
 HG[(HugeGraph)]
 CSV[Local CSV]
 HDFS[HDFS]
 end
 API --> HTTP
 UI --> HTTP
 GRPC_M <--> W1
 GRPC_M <--> W2
 GRPC_M <--> W3
 W1 -.-> HG
 W2 -.-> HG
 W3 -.-> HG
 W1 -.-> CSV
 W1 -.-> HDFS
 style Master fill:#e1f5fe
 style Workers fill:#f3e5f5
 style DataSources fill:#fff9c4

Component Overview:

┌─────────────────────────────────────────────────────────────┐
│ HugeGraph Ecosystem │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐ │
│ │ Hubble │ │ Toolchain │ │ HugeGraph-AI │ │
│ │ (UI) │ │ (Tools) │ │ (LLM/RAG) │ │
│ └──────┬──────┘ └──────┬──────┘ └────────┬────────┘ │
│ │ │ │ │
│ └──────────────────┼────────────────────┘ │
│ │ │
│ ┌───────▼───────┐ │
│ │ HugeGraph │ │
│ │ Server │ │
│ └───────┬───────┘ │
│ │ │
│ ┌──────────────────┼──────────────────┐ │
│ │ │ │ │
│ ┌──────▼──────┐ ┌──────▼──────┐ ┌─────▼─────┐ │
│ │ Vermeer │ │ Computer │ │ Store │ │
│ │ (Memory) │ │ (BSP/K8s) │ │ (PD) │ │
│ └─────────────┘ └─────────────┘ └───────────┘ │
└─────────────────────────────────────────────────────────────┘

For quick start and single-machine deployments, we recommend Vermeer:

# Pull the image
docker pull hugegraph/vermeer:latest
# Change config path in docker-compose.yaml
volumes:
 - ~/:/go/bin/config # Change here to your actual config path, e.g., vermeer/config
# Run with docker-compose
docker-compose up -d

# Download and extract (example for Linux AMD64)
wget https://github.com/apache/hugegraph-computer/releases/download/vX.X.X/vermeer-linux-amd64.tar.gz
tar -xzf vermeer-linux-amd64.tar.gz
cd vermeer
# Run master and worker
./vermeer --env=master &
./vermeer --env=worker &

See the Vermeer README for detailed configuration and usage.

For large-scale distributed graph processing on Kubernetes or YARN clusters, see the Computer README for:

• Prerequisites and build instructions
• Kubernetes/YARN deployment guide
• 45+ algorithm implementations
• Custom algorithm development framework

Features:

• In-memory optimized implementations
• REST API for algorithm execution
• Real-time result queries

Computer (Java) Algorithms: For Computer's 45+ algorithm implementations including distributed Triangle Count, Rings detection, and custom algorithm development framework, see Computer Algorithm List.

• ✅ Quick prototyping and experimentation
• ✅ Interactive analytics with built-in Web UI
• ✅ Graphs up to hundreds of millions of edges
• ✅ REST API integration requirements
• ✅ Single machine or small cluster with high-memory nodes
• ✅ Sub-second query response requirements

Performance: Optimized for fast iteration on medium-sized graphs with in-memory processing. Horizontal scaling by adding worker nodes.

• ✅ Billions of vertices/edges requiring distributed processing
• ✅ Existing Kubernetes or YARN infrastructure
• ✅ Custom algorithm development with Java
• ✅ Memory-constrained environments (auto disk spill)
• ✅ Integration with Hadoop ecosystem

Performance: Handles massive graphs via distributed BSP framework. Batch-oriented with superstep barriers. Elastic scaling on K8s.

• Project Homepage
• Vermeer Documentation
• Computer Documentation
• HugeGraph Documentation

1. hugegraph - Graph database core (Server + PD + Store)
2. hugegraph-toolchain - Graph tools (Loader/Hubble/Tools/Client)
3. hugegraph-ai - Graph AI/LLM/Knowledge Graph system
4. hugegraph-website - Documentation and website

Welcome to contribute to HugeGraph-Computer! Please see:

• How to Contribute for guidelines
• GitHub Issues for bug reports and feature requests

We recommend using GitHub Desktop to simplify the PR process.

Thank you to all contributors!

contributors graph

HugeGraph-Computer is licensed under Apache 2.0 License.

• GitHub Issues: Report bugs or request features
• Email: dev@hugegraph.apache.org (subscribe first)
• Slack: Join ASF HugeGraph channel
• WeChat: Scan QR code to follow Apache HugeGraph official account

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